docstr: Predict the input data
  
  :param data: predict input data.  It can be XShards or Spark DataFrame.
         If data is XShards, each partition can be a Pandas DataFrame or a dictionary of
         {'x': feature}, where feature is a numpy array or a tuple of numpy arrays.
  :param batch_size: Batch size used for inference. Default: None.
  :param verbose: Prints output of one model if true.
  :param steps: Total number of steps (batches of samples) before declaring the prediction
         round finished. Ignored with the default value of None.
  :param callbacks: List of Keras compatible callbacks to apply during prediction.
  :param data_config: An optional dictionary that can be passed to data creator function.
  :param feature_cols: Feature column name(s) of data. Only used when data is a Spark
         DataFrame or an XShards of Pandas DataFrame. Default: None.
  :return:
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code: def predict(
      self,
      data,
      batch_size=None,
      verbose=1,
      steps=None,
      callbacks=None,
      data_config=None,
      feature_cols=None,
  ):
      """
      Predict the input data
  
      :param data: predict input data.  It can be XShards or Spark DataFrame.
             If data is XShards, each partition can be a Pandas DataFrame or a dictionary of
             {'x': feature}, where feature is a numpy array or a tuple of numpy arrays.
      :param batch_size: Batch size used for inference. Default: None.
      :param verbose: Prints output of one model if true.
      :param steps: Total number of steps (batches of samples) before declaring the prediction
             round finished. Ignored with the default value of None.
      :param callbacks: List of Keras compatible callbacks to apply during prediction.
      :param data_config: An optional dictionary that can be passed to data creator function.
      :param feature_cols: Feature column name(s) of data. Only used when data is a Spark
             DataFrame or an XShards of Pandas DataFrame. Default: None.
      :return:
      """
      logger.info("Starting predict step.")
      params = dict(
          verbose=verbose,
          batch_size=batch_size,
          steps=steps,
          callbacks=callbacks,
          data_config=data_config,
      )
      from zoo.orca.data import SparkXShards
      from pyspark.sql import DataFrame
  
      if isinstance(data, DataFrame):
          xshards, _ = dataframe_to_xshards(
              data,
              validation_data=None,
              feature_cols=feature_cols,
              label_cols=None,
              mode="predict",
              accept_str_col=True,
          )
          pred_shards = self._predict_spark_xshards(xshards, params)
          result = convert_predict_xshards_to_dataframe(
              data, pred_shards
          )
      elif isinstance(data, SparkXShards):
          if data._get_class_name() == "pandas.core.frame.DataFrame":
              data = process_xshards_of_pandas_dataframe(
                  data, feature_cols
              )
          pred_shards = self._predict_spark_xshards(data, params)
          result = update_predict_xshards(data, pred_shards)
      else:
          raise ValueError(
              "Only xshards or Spark DataFrame is supported for predict"
          )
  
      return result
  
imports: import itertools
  import logging
  import pickle
  import numpy
  import ray
  from zoo.common.utils import enable_multi_fs_load, enable_multi_fs_save
  from zoo.orca.data.ray_xshards import RayXShards
  from zoo.orca.learn.dl_cluster import RayDLCluster
  from zoo.orca.learn.tf2.tf_runner import TFRunner
  from zoo.orca.learn.ray_estimator import Estimator
  from zoo.orca.learn.utils import maybe_dataframe_to_xshards, dataframe_to_xshards, convert_predict_xshards_to_dataframe, update_predict_xshards, process_xshards_of_pandas_dataframe
  from zoo.orca.data.utils import process_spark_xshards
  from zoo.ray import RayContext
label_desc: Use image node_id 5 for calling the TensorFlow2Estimator obj's underlying member method code with example usage: obj.predict(data, batch_size, verbose, steps, callbacks, data_config, feature_cols) and returns: result